面向无信号灯十字路口场景的自动驾驶安全决策方法研究

Research on Safe Decision-making for Autonomous Driving at Unsignalized Intersections

针对环境遮挡与交通参与者行为随机导致的驾驶风险,提出一种面向无信号灯十字路口场景的安全决策方法。首先,建立一种基于值分布式强化学习-全参数化分位数网络(Fully parameterized quantile network,FPQN)的基础决策策略。其次,融合FPQN建模的累积回报分布与条件风险价值函数(Conditional value at risk,CVaR),进而构建具有驾驶风险意识的安全决策策略。再次,引入集成学习理论(Ensemble),建立基于集成FPQN的决策不确定性估计框架EFPQN,能够实时量化决策风险。同时,为应对决策不确定性较高带来的驾驶风险,设计基于模型预测控制的备选策略以提升安全性。最后,采用SUMO仿真平台搭建无信号灯十字路口场景,对提出的安全决策方法进行验证。试验结果表明,与基准方法相比,所提出的方法能够有效降低遮挡与交通参与者行为随机导致的驾驶风险。

Proposes a safe decision-making method for unsignalized intersections,focusing on driving risks caused by environmental occlusion and the random behaviors of traffic participants.Firstly,a fundamental decision-making policy is established based on the value-distributional reinforcement learning-fully parameterized quantile network(FPQN).Secondly,the cumulative reward distribution modeled by FPQN and conditional value at risk(CVaR)are integrated to construct a driving risk-aware decision-making policy.Thirdly,the ensemble learning theory is introduced to establish the decision-making uncertainty estimation framework based on ensemble FPQN(EFPQN),which can quantify decision risks in real-time.Meanwhile,to handle the driving risk induced by high decision-making uncertainty,a model predictive control-based backup strategy is designed.Finally,the proposed safe decision-making method is validated using the SUMO simulation platform in an unsignalized intersection scenario.Experimental results show that the proposed method effectively reduces driving risks caused by environmental occlusion and random behaviors of traffic participants compared with baseline methods.